Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Springer International Publishing (Verlag)
978-3-030-59727-6 (ISBN)
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Brain Development and Atlases.- A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer's Disease.- A computational framework for dissociating development-related from individually variable flexibility in regional modularity assignment in early infancy.- Domain-invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains.- Parkinson's Disease Detection from fMRI-derived Brainstem Regional Functional Connectivity Networks.- Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification.- Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN based Generative Adversarial Network.- From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity.- Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting.- COVLET: Covariance-based Wavelet-like Transform for Statistical Analysis of Brain Characteristics in Children.- Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-Task Regression.- Self-weighted Multi-Task Learning for Subjective Cognitive Decline Diagnosis.- Unified Brain Network with Functional and Structural Data.- Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease.- Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features.- Masked Multi-Task Network for Case-level Intracranial Hemorrhage Classification in Brain CT Volumes.- Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates.- Supervised Multi-topology Network Cross-diffusion for Population-Driven Brain Network Atlas Estimation.- Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast.- BDB-Net: Boundary-enhanced DualBranch Network for Whole Brain Segmentation.- Brain Age Estimation From MRI Using a Two-Stage Cascade Network with a Ranking Loss.- Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation.- Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping.- Multi-Scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection.- Construction of Spatiotemporal Infant Cortical Surface Functional Templates.- DWI and Tractography.- Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles.- Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity.- White Matter Tract Segmentation with Self-supervised Learning.- Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.- Tractogram filtering of anatomically non-plausible fibers with geometric deep learning.- Unsupervised Deep Learning for Susceptibility Distortion Correction in Connectome Imaging.- Hierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis.- TRAKO: Efficient Transmission of Tractography Data for Visualization.- Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation.- Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein Framework.- Characterizing Intra-Soma Diffusion with Spherical Mean Spectrum Imaging.- Functional Brain Networks.- Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold.- Neural Architecture Search for Optimization of Spatial-temporal Brain Network Decomposition.- Attention-Guided Deep Graph Neural Network for Longitudinal Alzheimer's Disease Analysis.- Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis.- Whole MILC: generalizing learned dynamics across tasks, datasets, and populations.- A physics-informed geometric
| Erscheinungsdatum | 04.10.2020 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVII, 817 p. 30 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1288 g |
| Themenwelt | Schulbuch / Wörterbuch ► Unterrichtsvorbereitung ► Unterrichts-Handreichungen |
| Informatik ► Grafik / Design ► Digitale Bildverarbeitung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Sozialwissenschaften | |
| Schlagworte | Applications • Artificial Intelligence • Computer Aided Diagnosis • Computer Science • computer vision • conference proceedings • Image Analysis • Image Processing • image reconstruction • Image Segmentation • Imaging Systems • Informatics • machine learning • Medical Images • network architecture • Network Protocols • Neural networks • Research • segmentation methods • Signal Processing |
| ISBN-10 | 3-030-59727-X / 303059727X |
| ISBN-13 | 978-3-030-59727-6 / 9783030597276 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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